| Course | DAM-7020 Predictive Analytics for Business Intelligence |
|---|---|
| Module | Module 5 |
| Paper type | Discussion forum: initial post and replies |
| Length | About 409 words |
| Format | Discussion post with APA 7 citations |
| School | Thomas Edison State University |
| Program | MBA in Data Analytics |
| Updated | October 2026 |
Free sample paper for DAM 7020 Module 5
Module 5 Discussion: Where Does Analytics Create an Advantage That Lasts?
The Edge Is in the Habit, Not the Software: Analytics and Competitive Advantage for a Regional Tire Chain
National tire chains have data science teams larger than our whole headquarters. If they forecast demand, why would our forecasting give us an edge?
Barney (1991) argued that a firm keeps a lead only while the resource beneath it pays off, is scarce, resists copying and has no ready replacement. Forecasting software is none of these: anyone can buy it or use R for free. A forecast by itself will not set us apart.
What may be rare and hard to imitate is the combination around it. Gupta and George (2016) developed a measure of big data analytics capability with three kinds of resources: tangible resources such as data and technology, human resources such as analytical and managerial skills, and intangible resources such as a data-driven culture and organizational learning, and found that this capability related to firm performance. Our tangible resources are modest. Our potential advantage lies elsewhere: store-level knowledge of New Jersey driving, weather and customer habits built over thirty years, and store managers who can act on a forecast within hours because they own their stores' schedules.
So the question is not whether to forecast, since national chains already do, but whether we can build a habit of using forecasts locally faster than a national chain can push decisions down to its stores. That is a capability built slowly through training, trust and feedback, which is exactly why it would be hard to copy.
Davenport and Harris (2007) observe that analytical competitors pair analytics with leadership commitment and an enterprise-wide approach. For us, enterprise-wide means every store manager, not a central team.
Measuring that capability matters too. I would track how quickly store managers act on a forecast, for example how many hours pass between a cold weather forecast and a tire transfer, because speed of use is the advantage we are trying to build.
Speed matters more than sophistication.
Reply to Sienna
Sienna, your regional grocer competes with national chains that run dynamic pricing. Is your advantage local product knowledge, such as which stores sell more Portuguese or Korean items, and could analytics strengthen that rather than imitate their pricing?
Reply to Jamal
Jamal, your online retailer has the same tools as everyone else. What data do you collect that competitors cannot, such as returns reasons from your own customers, and could that be the rare resource?
References
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064. https://doi.org/10.1016/j.im.2016.07.004
Reading the DAM 7020 Module 5 assignment instructions
Together the five DAM-7020 forums count for 30 percent, with a first post and replies due in each. Module 5 asks how analytics can create competitive advantage. Apply a theory of advantage, such as the resource-based view, to an organization's analytics, identify which resources could be rare and hard to imitate and say what the organization should build. Support the argument with the course text and research on analytics capabilities. Replies should help classmates find what is distinctive in their own organizations' data or skills. Distinguish resources that can be bought from those that must be built. Cite research on analytics capability. Explain what the organization should build, not only what it has.
How this DAM 7020 Module 5 example is built
The post opens with a fair challenge: national chains have bigger data teams. Barney's criteria show why software alone cannot create lasting advantage. Gupta and George's three kinds of analytics resources locate the chain's possible edge in local knowledge and store managers' ability to act quickly. A margin note separates what can be bought from what must be built. The post reframes the question as building a habit of local use faster than national competitors, and Davenport and Harris's enterprise-wide view is applied to store managers. Replies ask a grocer about local product knowledge and an online retailer about unique data. The post closes with a measure of how quickly forecasts are used. The resource-based view separates what rivals can buy from what they cannot.
DAM 7020 Module 5 rubric: what earns full marks
Posts earn credit for accurate use of strategy and analytics capability research, application to a specific competitive situation, a clear argument and replies that help classmates. Using the resource-based view to separate imitable from inimitable resources shows strategic reasoning. Identifying human and intangible resources, not only technology, shows understanding of analytics capability. A concrete implication for what to build shows managerial judgment. Replies that look for distinctive resources in classmates' settings advance the discussion. Proposing a way to measure the capability being built shows rigor. Using a recognized theory of advantage, rather than assertion, strengthens the argument. Replies that look for each classmate's rare resource add the most.
DAM 7020 Module 5 help: mistakes that cost marks
Weak posts claim that analytics automatically creates advantage, focus only on technology or ignore competitors' ability to copy. Apply a theory of advantage, ask which of your organization's analytics resources are rare and hard to imitate, and say how to build them. Help classmates find what is distinctive in their own data or people. A free first draft of your DAM-7020 Module 5 post can be prepared on your organization's competitive position. Suggest how you would measure the advantage you describe. Ask whether a competitor could copy your analytics advantage by buying the same tools. Look for local knowledge and habits as sources of advantage.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More DAM 7020 and MBA in Data Analytics sample papers
- DAM 7020 Module 1: Discussion: Descriptive, Predictive or Prescriptive Analytics for a Tire and Auto Service Chain?
- DAM 7020 Module 2: Written Assignment: Trends in Business Analytics and What They Mean for an Auto Service Chain
- DAM 7020 Module 3: Midterm Paper: Why Predictive Modeling Matters, With Driving and Fuel Data for a Service Chain
- DAM 7020 Module 4: Discussion: R for a Forecasting Pilot, and What a Small Analytics Team Needs From Software
- DAM 7020 Module 6: Final Project: Forecasting Tire and Brake Demand in R for a Forty-Store Service Chain
- DAA 7030 Module 6: Capstone Project: A Data-Driven Marketing Plan for a Seventh Fitness Studio
- MST 7000 Module 1: Written Assignment: Describing Sixty Furniture Orders in Excel
- BFO 7010 Module 2: Written Assignment: Simple Forecasting Methods and Accuracy
DAM 7020 Module 5 questions, answered
Where can I find a free DAM 7020 Module 5 sample paper?
The discussion post above applies the resource-based view to analytics at a regional tire and auto service chain, with replies.
Can analytics create sustained competitive advantage?
Only if it rests on something rivals cannot simply buy, such as local know-how and habits of use; software alone rarely qualifies.
What is a big data analytics capability?
Gupta and George describe it as combining tangible, human and intangible resources, including a data-driven culture.
What is the resource-based view?
Barney's argument that durable advantage needs assets that are useful, scarce, hard to copy and without a substitute.
How many discussions does DAM-7020 have?
There are five, which together count for 30 percent.